Learning to Edit Interactive Machine Learning Notebooks

Machine learning (ML) developers frequently use interactive computational notebooks, such as Jupyter notebooks, to host code for data processing and model training. Notebooks provide a convenient tool for writing ML pipelines and interactively observing outputs. However, maintaining notebooks, e.g., to add new features or fix bugs, can be challenging due to the length and complexity of the ML pipeline code. Moreover, there is no existing benchmark related to developer edits on notebooks.